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E-book Bayesian Filter Design for Computational Medicine : A State-Space Estimation Framework

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Penilaian

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The human body is an intricate network of multiple functioning sub-systems. Many unobserved processes quietly keep running within the body even while we remain largely unconscious of them. For decades, scientists have sought to understand how different physiological systems work and how they can be mathematically modeled. Mathematical models of biological systems provide key scientific insights and also help guide the development of technologies for treating disorders when proper functioning no longer occurs. One of the challenges encountered with physiological systems is that, in a number of instances, the quantities we are interested in are difficult to observe directly or remain completely inaccessible. This could be either because they are located deep within the body or simply because they are more abstract (e.g., emotion). Consider the heart, for instance. The left ventricle pumps out blood through the aorta to the rest of the body. Blood pressure inside the aorta (known as central aortic pressure) has been considered a useful predictor of the future risk of developing cardiovascular disease, perhaps even more useful than the conventional blood pressure measurements taken from the upper arm [1]. However, measuring blood pressure inside the aorta is difficult. Consequently, researchers have had to rely on developing mathematical models with which to estimate central aortic pressure using other peripheral measurements (e.g., [2]). The same could be said regarding the recovery of CRH (corticotropin-releasing hormone) secretion timings within the hypothalamus—a largely inaccessible structure deep within the brain—using cortisol measurements in the blood based on mathematical relationships [3]. Emotions could also be placed in this same category. They are difficult to measure because of their inherently abstract nature. Emotions, however, do cause changes in heart rate, sweating, and blood pressure that can be measured and with which someone’s feelings can be estimated. What we have described so far, in a sense, captures the big picture underlying this book. We have physiological quantities that are difficult to observe directly, we have measurements that are easier to acquire, and we have the ability to build mathematical models to estimate those inaccessible quantities. Let us now consider some examples where the quantities we are interested in are rather abstract. Consider a situation where new employees at an organization are being taught a new task to be performed at a computer. Let us assume that each employee has a cognitive “task learning” state. Suppose also that the training sessions are accompanied by short quizzes at the end of each section. If we were to record how the employees performed (e.g., how many answers they got correct and how much time they took), could we somehow determine this cognitive learning state, and see how it gradually changes over time? The answer indeed is yes, with the help of a mathematical model, we can estimate such a state and track an employee’s progress over time. We will, however, first need to build such a model that relates learning to quiz performance. As you can see, the basic idea of building models that relate difficult-to-access quantities to measurements that we can acquire more easily and then estimate those quantities is a powerful concept. In this book, we will see how state-space models can be used to relate physiological/behavioral variables to experimental measurements.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

610.28 WIC b

Penerbit

Switzerland : Springer Nature., 2024

Deskripsi Fisik

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Bahasa

English

ISBN/ISSN

9783031471049

Klasifikasi

610.28

Tipe Isi

text

Tipe Media

computer

Tipe Pembawa

online resource

Edisi

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Subjek

Auxiliary Techniques

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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